Pansharpening aims at fusing a multispectral and a panchromatic image, featuring the result of the processing with the spectral resolution of the former and the spatial resolution of the latter. In the last decades, many algorithms addressing this task have been presented in the literature. However, the lack of universally recognized evaluation criteria, available image data sets for benchmarking, and standardized implementations of the algorithms makes a thorough evaluation and comparison of the different pansharpening techniques difficult to achieve. In this paper, the authors attempt to fill this gap by providing a critical description and extensive comparisons of some of the main state-of-the-art pansharpening methods. In greater details, several pansharpening algorithms belonging to the component substitution or multiresolution analysis families are considered. Such techniques are evaluated through the two main protocols for the assessment of pansharpening results, i.e., based on the full- and reduced-resolution validations. Five data sets acquired by different satellites allow for a detailed comparison of the algorithms, characterization of their performances with respect to the different instruments, and consistency of the two validation procedures. In addition, the implementation of all the pansharpening techniques considered in this paper and the framework used for running the simulations, comprising the two validation procedures and the main assessment indexes, are collected in a MATLAB toolbox that is made available to the community.

A Critical Comparison Among Pansharpening Algorithms

Remote sensing-based methods of aboveground biomass (AGB) estimation in forest ecosystems have gained increased attention, and substantial research has been conducted in the past three decades. This paper provides a survey of current biomass estimation methods using remote sensing data and discusses four critical issues – collection of field-based biomass reference data, extraction and selection of suitable variables from remote sensing data, identification of proper algorithms to develop biomass estimation models, and uncertainty analysis to refine the estimation procedure. Additionally, we discuss the impacts of scales on biomass estimation performance and describe a general biomass estimation procedure. Although optical sensor and radar data have been primary sources for AGB estimation, data saturation is an important factor resulting in estimation uncertainty. LIght Detection and Ranging (lidar) can remove data saturation, but limited availability of lidar data prevents its extensive application. This...

A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems

Review of the pansharpening methods for remote sensing images based on the idea of meta-analysis: Practical discussion and challenges

. Permafrost coasts in the Arctic are susceptible to a variety of changing environmental factors all of which currently point to increasing coastal erosion rates and mass fluxes of sediment and carbon to the shallow arctic shelf seas. Rapid erosion along high yedoma coasts composed of Ice Complex permafrost deposits creates impressive coastal ice cliffs and inspired research for designing and implementing change detection studies for a long time, but continuous quantitative monitoring and a qualitative inventory of coastal thermo-erosion for large coastline segments is still lacking. Our goal is to use observations of thermo-erosion along the mainland coast of the Laptev Sea, in eastern Siberia, to understand how it depends on coastal geomorphology and the relative contributions of water level and atmospheric drivers. We compared multi-temporal sets of orthorectified satellite imagery from 1965 to 2011 for three segments of coastline ranging in length from 73 to 95 km and analyzed thermo-denudation (TD) along the cliff top and thermo-abrasion (TA) along the cliff bottom for two nested time periods: long-term rates (the past 39–43 yr) and short-term rates (the past 1–4 yr). The Normalized Difference Thermo-erosion Index (NDTI) was used as a proxy to qualitatively describe the relative proportions of TD and TA. Mean annual erosion rates at all three sites were higher in recent years (−5.3 ± 1.3 m a−1) than over the long-term mean (−2.2 ± 0.1 m a−1). The Mamontov Klyk coast exhibits primarily spatial variations of thermo-erosion, while intrasite-specific variations caused by local relief were strongest at the Buor Khaya coast, where the slowest long-term rates of around −0.5 ± 0.1 m a−1 were observed. The Oyogos Yar coast showed continuously rapid erosion up to −6.5 ± 0.2 m a−1. In general, variable characteristics of coastal thermo-erosion were observed not only between study sites and over time, but also within single coastal transects along the cliff profile. Varying intensities of cliff bottom and top erosion are leading to diverse qualities of coastal erosion that have different impacts on coastal mass fluxes. The different extents of Ice Complex permafrost degradation within our study sites turned out to influence not only the degree of coupling between TD and TA, and the magnitude of effectively eroded volumes, but also the quantity of organic carbon released to the shallow Laptev Sea from coastal erosion, which ranged on a long-term from 88 ± 21 to 800 ± 61 t per km coastline per year and will correspond to considerably higher amounts, if recently observed more rapid coastal erosion rates prove to be persistent.

/pdf/short-and-long-term-thermo-erosion-of-ice-rich-permafrost-3c325lmv1q.pdf

Short- and long-term thermo-erosion of ice-rich permafrost coasts in the Laptev Sea region

Characterized by a certain focus on the heavily discussed topic of image fusion in its beginnings, sensor data fusion has played a significant role in the remote sensing research community for a long time. With this article, we aim to provide a short overview of established definitions, targeting a generalized understanding of the topic. In addition, a review of the state of the art of remote sensing data fusion research is given. By bringing together the conventional view expressed in the classical data fusion community and a review of current activities in the field of Earth observation, this article provides a holistic view of generic data fusion concepts and their applicability to the remote sensing domain.

Data Fusion and Remote Sensing: An ever-growing relationship

The main objective of this article is quality assessment of pansharpening fusion methods. Pansharpening is a fusion technique to combine a panchromatic image of high spatial resolution with multispectral image data of lower spatial resolution to obtain a high-resolution multispectral image. During this process, the significant spectral characteristics of the multispectral data should be preserved. For images acquired at the same time by the same sensor, most algorithms for pansharpening provide very good results, i.e. they retain the high spatial resolution of the panchromatic image and the spectral information from the multispectral image (single-sensor, single-date fusion). For multi-date, multi-sensor fusion, however, these techniques can still create spatially enhanced data sets, but usually at the expense of the spectral consistency. In this study, eight different methods are compared for image fusion to show their ability to fuse multitemporal and multi-sensor image data. A series of eight multitemp...

https://www.tandfonline.com/doi/pdf/10.1080/19479830903561985

Multi-sensor image fusion for pansharpening in remote sensing

Image fusion is a technique that is used to combine the spatial structure of a high resolution panchromatic image with the spectral information of a low resolution multispectral image to produce a high resolution multispectral image. In this paper, first results of fusion experiments with TerraSARX and optical multispectral image data will be presented. To generate fused images, use was made of the Ehlers fusion, a fusion technique that is developed for preserving maximum spectral information. As a result of a changeable filter setting within the Ehlers fusion, three differently fused images were created. To assess the quality of the fusion process, visually and quantitative analyses were performed. First, each band of the fused image was visually compared to the respective original multispectral band for preservation of the original spectral characteristics. Then, the identical band combinations of the fused and original images were compared, such as true color or false color infrared composites. For the spatial resolution evaluation of the fused images, their spectral bands of the fused images were compared to the high resolution TerraSAR-X image. Particular attention was paid to changes of contrast and gray values near edges like streets and buildings. Due to the fact that visual comparison is very subjective; the fused images were also evaluated using quantitative and statistical methods. Techniques that were employed included correlation analysis, calculation of per-pixel deviation, structure similarity index (SSIM), high pass filtering, and edge detection techniques. Best overall performance was achieved by a filter design that represented a compromise between ultimate spatial enhancement and optimum color preservation.

Image fusion of high resolution terrasar-x and multispectral electro-optical data for improved spatial resolution

Prüfung von Luftbilddaten zweier unterschiedlicher Aufnahmesensoren hinsichtlich eines optimalen Aufnahmesystems zur Erfüllung von Aufgaben von Vermessungs- und Umweltverwaltung für das Landesamt für Natur und Umwelt

Pansharpening is an image fusion technique that combines the spatial structure of a high resolution panchromatic image
with the spectral information of a lower resolution multispectral image to produce a high resolution multispectral image.
Image data of the new German RADAR satellite TerraSAR-X were used to sharpen optical multispectral data. To assess
the advantages and limitations of fusion, the interpretability of terrain features at different image resolutions was
determined. We concluded that a resolution ratio of 1:10 (TerraSAR:Multispectral) is optimal to benefit from the
synergism of a SAR/multispectral fusion. Q index and an object-based classification were used to assess fusion quality
and to compare their efficacy to determine the best fusion algorithms. Both approaches are appropriate methods to asses
quality but only on judging some aspects of the fusion products. We conclude that a more comprehensive fusion quality
assessment method still needs to be developed.

Pablo Rosso

Papers

Multi-sensor image fusion for pansharpening in remote sensing

Image fusion of high resolution terrasar-x and multispectral electro-optical data for improved spatial resolution

Prüfung von Luftbilddaten zweier unterschiedlicher Aufnahmesensoren hinsichtlich eines optimalen Aufnahmesystems zur Erfüllung von Aufgaben von Vermessungs- und Umweltverwaltung für das Landesamt für Natur und Umwelt

Interpretability of TerraSAR-X fused data